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        <h1 id="pandas中Dataframe的一些用法"><a href="#pandas中Dataframe的一些用法" class="headerlink" title="pandas中Dataframe的一些用法"></a>pandas中Dataframe的一些用法</h1><h4 id="pandas读取excel文件"><a href="#pandas读取excel文件" class="headerlink" title="pandas读取excel文件"></a>pandas读取excel文件</h4><ul>
<li><strong>pd.read_excel</strong> 前提是安装xlrd库</li>
</ul>
<h4 id="dataframe，numpy，list之间的互相转换"><a href="#dataframe，numpy，list之间的互相转换" class="headerlink" title="dataframe，numpy，list之间的互相转换"></a>dataframe，numpy，list之间的互相转换</h4><ul>
<li>dataframe转numpy ：dataframe对象.values</li>
<li>dataframe转list：dataframe对象.values.tolist()</li>
<li>list转numpy：np.array(list对象)</li>
<li>list转dataframe：pd.DataFrame(list对象)</li>
<li>numpy转list：numpy对象.tolist()</li>
<li>numpy转dataframe:pd.DataFrame(numpy对象)</li>
</ul>
<span id="more"></span>

<h4 id="dataframe-按行遍历，按列遍历"><a href="#dataframe-按行遍历，按列遍历" class="headerlink" title="dataframe 按行遍历，按列遍历"></a>dataframe 按行遍历，按列遍历</h4><ul>
<li><p>按行遍历:</p>
<p>常用df.iterrows()</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd </span><br><span class="line">demo_list = [[<span class="number">1</span>,<span class="number">2</span>],</span><br><span class="line">             [<span class="number">3</span>,<span class="number">4</span>]]</span><br><span class="line"><span class="comment">#用list构建dataframe</span></span><br><span class="line">demo_df = pd.DataFrame(demo_list)</span><br><span class="line"><span class="built_in">print</span>(demo_df)</span><br><span class="line"></span><br></pre></td></tr></table></figure></li>
</ul>
<p><img src="https://img-blog.csdnimg.cn/20201215202443394.png" alt="在这里插入图片描述"></p>
  <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#接上</span></span><br><span class="line"><span class="keyword">for</span> row <span class="keyword">in</span> demo_df.iterrows():</span><br><span class="line">	<span class="built_in">print</span>(<span class="built_in">type</span>(row))</span><br><span class="line">    <span class="built_in">print</span>(row[<span class="number">0</span>])</span><br><span class="line">    <span class="built_in">print</span>(row[<span class="number">1</span>])</span><br></pre></td></tr></table></figure>

<p><img src="https://img-blog.csdnimg.cn/20201215202501750.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMzczMjEy,size_16,color_FFFFFF,t_70" alt="在这里插入图片描述"></p>
<p>  可以看到每个row的类型是tuple元组类型，元组长度为2，元组第0个元素为index，第1个元素为横向的series。**值得注意的是，在遍历过程中如果取每一行的某个值，通过对row[1]进行切片即可。 **</p>
<ul>
<li><p>按列遍历</p>
<p>经常使用df.columns获取列名然后访问</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#接上</span></span><br><span class="line"><span class="built_in">print</span>(demo_df.columns)</span><br><span class="line"><span class="keyword">for</span> column <span class="keyword">in</span> demo_df.columns:</span><br><span class="line">    <span class="built_in">print</span>(demo_df[column])</span><br></pre></td></tr></table></figure></li>
</ul>
<p><img src="https://img-blog.csdnimg.cn/20201215202518743.png" alt="在这里插入图片描述"></p>
<h4 id="dataframe之使用iloc切片"><a href="#dataframe之使用iloc切片" class="headerlink" title="dataframe之使用iloc切片"></a>dataframe之使用iloc切片</h4><ul>
<li>先构建dataframe</li>
</ul>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="comment">##list构建5x5的dataframe，由于dataframe没有reshape，因此需要借助numpy</span></span><br><span class="line">demo_list = [i <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">25</span>)]</span><br><span class="line">demo_np = np.array(demo_list).reshape(<span class="number">5</span>,<span class="number">5</span>)</span><br><span class="line">demo_df = pd.DataFrame(demo_list)</span><br><span class="line"><span class="built_in">print</span>(demo_df)</span><br></pre></td></tr></table></figure>

<p><img src="https://img-blog.csdnimg.cn/20201215202540538.png" alt="在这里插入图片描述"></p>
<ul>
<li><strong>iloc[start:end ,start :end ]</strong> 表示按行列取出dataframe的值。<strong>其中逗号前面表示行，逗号后面表示列。冒号左侧表示开始，冒号右侧表示结束(遵循左闭右开原则)。例如，demo_df.iloc[2:4,1:3]表示切片第二行到第三行 第一列到第二列数据。</strong> 切片返回的数据类型还是dataframe。</li>
</ul>
<p><img src="https://img-blog.csdnimg.cn/20201215202558863.png" alt="在这里插入图片描述"></p>
<ul>
<li><strong>iloc[start: end :step,start:end :step]</strong> 是在上一个切片的基础上加上了步长。表示从start到end每step步取一次值。</li>
</ul>
<h4 id="dataframe-中缺失值的处理"><a href="#dataframe-中缺失值的处理" class="headerlink" title="dataframe 中缺失值的处理"></a>dataframe 中缺失值的处理</h4><ul>
<li><p>均值填充</p>
<p>通常使用fillna()</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">##获取存在缺失值的列名列表</span></span><br><span class="line">null_columns=<span class="built_in">list</span>(file_df.columns[file_df.isnull().<span class="built_in">sum</span>() &gt; <span class="number">0</span>])</span><br><span class="line"><span class="keyword">for</span> column <span class="keyword">in</span> null_columns :</span><br><span class="line">    <span class="comment">#计算每一列的均值</span></span><br><span class="line">	mean_val = file_df[column].mean()</span><br><span class="line">	<span class="comment">#使用fillna进行均值填充</span></span><br><span class="line">    file_df[column].fillna(mean_val, inplace=<span class="literal">True</span>)  </span><br></pre></td></tr></table></figure></li>
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